How saiteja74/laptop_price_predictor works
This is a small Streamlit laptop-price estimator. Users select laptop specifications, the app derives screen PPI and encodes touchscreen/IPS choices, then sends the resulting feature row to a pickled prediction pipeline and displays an estimated price range. The sampled app.py explicitly loads traineddata.csv for selectable values and pipe.pkl for inference; other serialized and CSV files are present in the tree but their runtime use is not shown.
Main entry points: app.py Feature preparation · pipe.pkl Prediction pipeline · traineddata.csv Option catalog
Components 6
User interface
- Specification form
app.py - Price range
app.py
Prediction runtime
- Feature preparation
app.py
Runtime assets
- Prediction pipeline
pipe.pkl - Option catalog
traineddata.csv
Other components
- Laptop shopper
Connections 7
- Laptop shopper → Specification form: enters specs
- Specification form → Option catalog: reads options
- Specification form → Feature preparation: submits specs
- Feature preparation → Prediction pipeline: predicts price
- Prediction pipeline → Feature preparation: returns prediction
- Feature preparation → Price range: formats range
- Price range → Laptop shopper: displays estimate